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      KCI등재 SCOPUS

      Semiparametric ARCH-X Model for Leverage Effect and Long Memory in Stock Return Volatility

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      https://www.riss.kr/link?id=A100140552

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      다국어 초록 (Multilingual Abstract)

      This paper investigates a new semiparametric ARCH-X model to account for both leverage effect and long memory property in volatility. It is a partial linear model combining a nonparametric ARCH component and an exogenous covariate that is persistent in memory. This model can allow for a flexible functional form of the asymmetric relationship between stock return and volatility and generate the long memory property in volatility. We adopt a realized volatility measure as the covariate in our model. For the daily FTSE 100 Index return series, the nonparametric component of our model captures the leverage effect and is estimated to be a complex nonlinear function. It is shown that our model outperforms other parametric or nonparametric volatility models both in within-sample and out-of-sample forecasts.
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      This paper investigates a new semiparametric ARCH-X model to account for both leverage effect and long memory property in volatility. It is a partial linear model combining a nonparametric ARCH component and an exogenous covariate that is persistent i...

      This paper investigates a new semiparametric ARCH-X model to account for both leverage effect and long memory property in volatility. It is a partial linear model combining a nonparametric ARCH component and an exogenous covariate that is persistent in memory. This model can allow for a flexible functional form of the asymmetric relationship between stock return and volatility and generate the long memory property in volatility. We adopt a realized volatility measure as the covariate in our model. For the daily FTSE 100 Index return series, the nonparametric component of our model captures the leverage effect and is estimated to be a complex nonlinear function. It is shown that our model outperforms other parametric or nonparametric volatility models both in within-sample and out-of-sample forecasts.

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      목차 (Table of Contents)

      • Abstract
      • 1. INTRODUCTION
      • 2. MODEL AND ESTIMATION METHOD
      • 3. EMPIRICAL APPLICATION
      • 4. CONCLUSION
      • Abstract
      • 1. INTRODUCTION
      • 2. MODEL AND ESTIMATION METHOD
      • 3. EMPIRICAL APPLICATION
      • 4. CONCLUSION
      • REFERENCES
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      참고문헌 (Reference)

      1 Patton, A. J., "Volatility forecast comparison using imperfect volatility proxies" 160 : 246-256, 2011

      2 Han, H., "Time series properties of ARCH processes with persistent covariates" 146 : 275-292, 2008

      3 Wang, Q., "Structural nonparametric cointegrating regression" 77 : 1901-1948, 2009

      4 Linton, O.B., "Semi- and nonparametric ARCH processes" 2011 : 1-17, 2011

      5 Robinson, P. M., "Root-n-consistent semiparametric regression" 56 : 931-954, 1988

      6 Hansen, P.R., "Realized GARCH: a joint model for returns and realized measures of volatility" 27 : 877-906, 2012

      7 Shephard, N., "Realising the future : forecasting with high frequency based volatility(HEAVY)models" 25 : 197-231, 2010

      8 Dittmann, I., "Properties of nonlinear transformations of fractionally integrated processes" 110 : 113-133, 2002

      9 Glosten, L. R., "On the relation between the expected value and the volatility of nominal excess returns on stocks" 48 : 1779-1801, 1993

      10 Granger, C.W.J., "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns" 11 : 399-421, 2004

      1 Patton, A. J., "Volatility forecast comparison using imperfect volatility proxies" 160 : 246-256, 2011

      2 Han, H., "Time series properties of ARCH processes with persistent covariates" 146 : 275-292, 2008

      3 Wang, Q., "Structural nonparametric cointegrating regression" 77 : 1901-1948, 2009

      4 Linton, O.B., "Semi- and nonparametric ARCH processes" 2011 : 1-17, 2011

      5 Robinson, P. M., "Root-n-consistent semiparametric regression" 56 : 931-954, 1988

      6 Hansen, P.R., "Realized GARCH: a joint model for returns and realized measures of volatility" 27 : 877-906, 2012

      7 Shephard, N., "Realising the future : forecasting with high frequency based volatility(HEAVY)models" 25 : 197-231, 2010

      8 Dittmann, I., "Properties of nonlinear transformations of fractionally integrated processes" 110 : 113-133, 2002

      9 Glosten, L. R., "On the relation between the expected value and the volatility of nominal excess returns on stocks" 48 : 1779-1801, 1993

      10 Granger, C.W.J., "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns" 11 : 399-421, 2004

      11 Heber, G., "OMI’s realised library, Version 0.1"

      12 Park, J. Y., "Nonstationary nonlinear heteroskedasticity" 110 : 383-415, 2002

      13 Mikosch, T., "Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects" 86 : 378-390, 2004

      14 Karlesn, H.A., "Nonparametric estimation in null recurrent time series" 29 : 372-416, 2001

      15 Karlsen, H. A., "Nonparametric estimation in a nonlinear cointegration type model" 35 : 252-299, 2007

      16 Masry, E., "Nonparametric estimation and identification of nonlinear ARCH time series : strong convergence and asymptotic normality" 11 : 258-289, 1995

      17 Pagan, C.R., "Nonparametric and Semiparametric Methods in Econometrics and Statistics" Cambridge University Press 1991

      18 Han, H., "Non-stationary non-parametric volatility model" 15 : 204-225, 2012

      19 Engle, R. F., "New frontiers for ARCH models" 17 : 425-446, 2002

      20 Ding, Z., "Modeling volatility persistence of speculative returns : A new approach" 73 : 185-215, 1996

      21 Bollerslev, T., "Modeling and pricing long-memory in stock market volatility" 73 : 151-184, 1996

      22 Andersen, T.G., "Modeling and forecasting realized volatility" 71 : 529-626, 2003

      23 Diebold, F.X., "Long memory and regime switching" 105 : 131-159, 2001

      24 Speckman, P., "Kernel smoothing in partial linear models" 50 : 413-446, 1988

      25 Patton, A.J., "Handbook of Financial Time Series" Springer 801-838, 2009

      26 Linton, O. B., "Handbook of Financial Time Series" Springer 157-168, 2009

      27 Baillie, R.T., "Fractionally integrated generalized autoregressive conditional heteroskedasticity" 74 : 3-30, 1996

      28 Fan, J., "Efficient estimation of conditional variance functions in stochastic regression" 85 : 645-660, 1998

      29 Barndorff-Nielsen, O.E., "Econometric analysis of realized volatility and its use in estimating stochastic volatility models" 64 : 253-280, 2002

      30 Barndorff-Nielsen, O. E., "Designing realized kernels to measure the ex-post variation of equity prices in the presence of noise" 76 : 1481-1536, 2008

      31 Hansen, P.R., "Consistent ranking of volatility models" 131 : 97-121, 2006

      32 Nelson, D. B., "Conditional heteroskedasticity in asset returns : A new approach" 59 : 347-370, 1991

      33 Diebold, F.X., "Comparing predictive accuracy" 13 : 253-263, 1995

      34 Engle, R.F., "Cointegration, Causality, and Forecasting: A Festschrift in Honour of Clive W.J. Granger" Oxford University Press 475-497, 1999

      35 Andrews, D.W.K., "Asymptotics for semiparametric econometric models via stochastic equicontinuity" 62 : 43-72, 1994

      36 Park, J.Y., "Asymptotics for nonlinear transformations of integrated time series" 15 : 269-298, 1999

      37 Wang, Q., "Asymptotic theory for local time density estimation and nonparametric cointegrating regression" 25 : 1-29, 2009

      38 West, K. D., "Asymptotic inference about predictive ability" 64 : 1067-1084, 1996

      39 Han, H., "Asymptotic Properties of GARCH-X processes" 2014

      40 Pagan, A.R., "Alternative models for conditional stock volatility" 45 : 267-290, 1990

      41 Barndorff-Nielsen, O.E., "Advances in Economics and Econometrics. Theory and Applications, Ninth World Congress, Econometric Society Monographs" Cambridge University Press 328-372, 2007

      42 Engle, R.F., "A multiple indicators model for volatility using intra-daily data" 131 : 3-27, 2006

      43 Cipollini, F., "A model for multivariate nonnegative valued processes in financial econometrics" Stern School of Business, New York University 2007

      44 Ding, Z., "A long memory property of stock market returns and a new model" 1 : 83-106, 1993

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      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-04-10 통합 KCI등재
      2020-04-01 학술지명변경 외국어명 : Journal of Economic Theory and Econometrics(JETEM) -> Journal of Economic Theory and Econometrics KCI등재
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2014-03-01 평가 SCOPUS 등재 (기타) KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2007-12-01 학술지명변경 외국어명 : 미등록 -> Journal of Economic Theory and Econometrics(JETEM) KCI등재
      2007-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2004-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2003-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2002-01-01 평가 등재후보학술지 유지 (등재후보1차) KCI등재후보
      1999-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.09 0.09 0.08
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.09 0.07 0.363 0.06
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